The embedding layer transforms each feature into a dense vector to facilitate learning efficiency.
For numerical features that are infeasible to embed, feature discretization techniques [1]}[2]}[3]} are conducted, which are proven useful to improve the learning efficiency [4]}.
The embedding layer converts each feature vector \(\mathop x\nolimits _i \in \mathop {\mathcal {R}}\nolimits ^d\)  into an embedded representation \(\mathop e\nolimits _i \in \mathop {\mathcal {R}}\nolimits ^{d \times k}\) , where \(k\)  is the embedding dimensionality.
Afterward, the embedded representation is flattened to form a one-dimensional embedded vector \(\mathop e\nolimits _i \in \mathop {\mathcal {R}}\nolimits ^{dk}\) .
Therefore, after embedding, the data samples \(\mathcal {S} \in \mathop {\mathcal {R}}\nolimits ^{N \times d}\)  will become \(E \in \mathop {\mathcal {R}}\nolimits ^{N \times dk}\) .
